NGFWs use advanced security technologies such as malware protection, sandboxing, and threat intelligence. They also reduce attack vectors and offer granular application control to prevent data leakage. NGFWs use deep packet inspection to inspect network traffic and detect cyber threats. This makes them more than a network layer tool and higher than the session layer.
A malware attack involves sending malicious code to the targeted network to hijack resources and steal data. NGFW helps detect and stop these attacks by performing deep packet inspection. This is a sophisticated form of packet filtering that examines not only the layer 3 and 4 headers but also the actual contents of each packet to check for potential threats like malware signatures.
Moreover, NGFWs also offer application awareness and control features that reduce the number of possible attack vectors by blocking applications not included in the allowlist. This is essential since hackers can use steganography tools to conceal malicious content in otherwise harmless traffic and fool the firewall.
An ML-Powered NGFW uses machine learning to turn the firewall into a proactive security control point. It does this by continuously learning from massive amounts of threat data. Inline ML analyzes the data to detect threats faster and more accurately than traditional firewalls. It can instantly prevent and block unknown threats, saving network administrators time.
NGFWs can help protect against ransomware, a cyber threats that encrypts files and locks the data until a ransom is paid. NGFWs can detect this by inspecting incoming and outgoing data to look for commands that encrypt the files. They can also monitor the status of in-network users and identify bandwidth-heavy activities that may require more resources. They can also block unauthorized access to sensitive data on the cloud.
Detecting Network Intrusions
NGFWs use deep packet inspection to detect threats and block them from entering the network, preventing unauthorized users from accessing internal resources. This technology is essential to a company’s security and privacy, preventing attackers from breaching a business’s internal systems and accessing sensitive data.
Unlike traditional firewalls, NGFWs analyze traffic at Layer 7, the application layer, to determine what an incoming data packet tries to do. This allows administrators to identify risky applications and prevent them from introducing malware into the network. This capability is called “application awareness” or “threat prevention,” a common feature in many NGFWs.
Advanced malware attacks are often distributed at scale and evolve quickly. Traditional defenses must adapt slowly because they require manual updates and take too long to inspect each file before detecting a new threat. ML-Powered NGFWs rearchitect how signatures are delivered, enabling them to instantly see and stop new threats.
IoT devices are being added to the corporate network at a dizzying pace, creating new security risks. Older NGFWs can’t keep up with the rate of IoT device additions or track unexpected behavior. ML-Powered NGFWs, on the other hand, can automatically group IoT devices by category and compare their metadata with that of the network to establish normal behavior patterns. They then proactively recommend policies allowing only the necessary IoT devices to access network resources, saving network administrators from updating them manually.
Detecting Data Leakage
Data leaks and breaches make up a significant portion of network security threats. They violate the integrity and confidentiality of sensitive information, which can then be used for malicious purposes or sold on the dark web. An NGFW can detect and prevent these attacks by scanning incoming data packets for suspicious and unauthorized activity, preventing the transmission of unsecured and confidential data.
NGFWs can inspect traffic across layers of the OSI model, including Layer 7, where applications communicate with users. This provides them with complete knowledge about the underlying threats trying to breach a network. This is in contrast to traditional firewalls, which only inspect the network layer and may be unable to stop smarter attacks.
Inline ML prevents new malware variants by scanning and analyzing a file as downloaded, making a real-time decision about its behavior. This enables it to block new threats as soon as they appear while reducing the time it takes to perform signature updates.
NGFWs are often equipped with features akin to Web Application Firewalls (WAF). This makes them capable of detecting SQL injections, a common attack vector for cybercriminals to exploit. These features help to reduce the risk of attacks on critical business data and ensure that priority is given to mission-critical applications.
Detecting DDoS Attacks
As sophisticated cyber actors and nation-states develop advanced capabilities to steal data, disrupt services, and threaten national security, enterprises must protect themselves. NGFW can detect attacks and take actions to shut down these malicious activities by inspecting the content of each incoming packet. This is known as deep packet inspection and is a key component of an NGFW.
Unlike traditional firewalls, which look at the IP address and port number in each packet to determine whether it is allowed into the network, an NGFW looks into the contents of each incoming package to ensure they match your policies. If the NGFW detects that a packet does not match its rules, it can send the incoming packet off to another device for further inspection. This makes a next-generation firewall an intrusion prevention system (IPS).
In addition to DDoS protection, NGFW can detect other types of threats like malware and advanced persistent threat (APT). An IPS in an NGFW can scan files for vulnerabilities that exploit weaknesses. It can also send suspicious files off-device and emulate them in a virtual environment to see if they behave negatively.
Many NGFWs are more than just hardware and include a suite of connected security solutions you can stack to create a complete security perimeter.